from datasets import load_dataset, Dataset
import pandas as pd
from collections import defaultdict
import pygments

list_languages = ['ada', 'agda', 'alloy', 'antlr', 'applescript', 'assembly', 'augeas', 'awk', 'batchfile', 'bison',
 'bluespec', 'c', 'c++', 'c-sharp', 'clojure', 'cmake', 'coffeescript', 'common-lisp', 'css', 'cuda', 'dart', 'dockerfile', 'elixir',
'elm', 'emacs-lisp','erlang', 'f-sharp', 'fortran', 'glsl', 'go', 'groovy', 'haskell','html', 'idris', 'isabelle', 'java',
'java-server-pages', 'javascript', 'stan', 'julia', 'kotlin', 'lean', 'literate-agda', 'literate-coffeescript', 'literate-haskell',
 'lua', 'makefile', 'maple', 'markdown', 'mathematica', 'matlab', 'ocaml', 'pascal', 'perl', 'php', 'powershell', 'prolog',
  'protocol-buffer', 'python', 'r', 'racket', 'restructuredtext', 'rmarkdown', 'ruby', 'rust', 'sas', 'scala', 'scheme', 
  'shell', 'smalltalk', 'solidity', 'sparql', 'sql', 'stan', 'standard-ml', 'stata', 'systemverilog', 'tcl', 'tcsh', 'tex', 
  'thrift', 'typescript', 'verilog', 'vhdl', 'visual-basic', 'xslt', 'yacc', 'zig']

lmap = {'c-sharp':'csharp', 'f-sharp':'fsharp', 'standard-ml':'sml', 'batchfile':'batch','java-server-pages':'jsp'}

extra_columns = [
            "hexsha",
            "max_stars_repo_path",
            "max_stars_repo_name",
            "max_stars_repo_head_hexsha",
            "max_stars_repo_stars_event_min_datetime",
            "max_stars_repo_stars_event_max_datetime",
            "max_issues_repo_path",
            "max_issues_repo_name",
            "max_issues_repo_head_hexsha",
            "max_issues_repo_licenses",
            "max_issues_count",
            "max_issues_repo_issues_event_min_datetime",
            "max_issues_repo_issues_event_max_datetime",
            "max_forks_repo_path",
            "max_forks_repo_name",
            "max_forks_repo_head_hexsha",
            "max_forks_repo_licenses",
            "max_forks_count",
            "max_forks_repo_forks_event_min_datetime",
            "max_forks_repo_forks_event_max_datetime",
        ]

seed = 0
size = 20_000
buffer_size = 40_000
max_data_per_ext = 1000
df = pd.DataFrame(
    columns=[
        "extension",
        "language",
        "count",
        "low_alphanum_count",
        "long_lines_count",
        "non_lexable_count",
    ]
)

def low_alphanum(example):
    return {"low_alphanum": example["alphanum_fraction"] < 0.25}

def long_line(example):
    return {"long_lines": example["max_line_length"] > 1000 or example["avg_line_length"] > 100}

def pygments_language_id_to_thestack_language_id(str):
    if str in lmap:
        return lmap[str]
    return str

def can_lex_without_errors(lexer, contents: str):
    tokens = pygments.lex(contents, lexer)
    for (tok_type, tok_text) in tokens:
        if tok_type == pygments.token.Token.Error:
            return False
    return True

def lexable(example, language):
    try:
        lexer = pygments.lexers.get_lexer_by_name(pygments_language_id_to_thestack_language_id(language))
    except:
        return {"lexable": "notfound"}
    return {"lexable": can_lex_without_errors(lexer, example["content"])}


for language in list_languages:
    thestack = load_dataset(
        "bigcode/the-stack",
        use_auth_token=True,
        split="train",
        streaming=True,
        data_dir=f"data/{language}",
    )
    thestack = thestack.shuffle(seed=seed, buffer_size=buffer_size)
    print(f"subset {language} ready, now selecting {size} samples")

    # 20k subset of random samples from ds, convert to Datasets
    small_ds = list(thestack.take(size))
    small_ds = Dataset.from_pandas(pd.DataFrame(data=small_ds))
    small_ds = small_ds.remove_columns(extra_columns)
    print(f"Dataset of {size} samples of {language} creaded")

    # get extension distribution
    dict_extensions = defaultdict(int)
    for extension in small_ds["ext"]:
        dict_extensions[extension] += 1
    dict_extensions = dict(dict_extensions)
    print(f"Initial extension dist: {dict_extensions}")

    # filter for extension
    for ext in dict_extensions:
        ext_ds = small_ds.filter(lambda x: x["ext"] == ext)
        real_count = min(max_data_per_ext, len(ext_ds))
        ext_ds = ext_ds.select(range(real_count))

        # let's add extra info
        ext_ds = ext_ds.map(low_alphanum)
        ext_ds = ext_ds.map(long_line)
        ext_ds = ext_ds.map(lambda x: lexable(x, language))

        low_alphanum_count = sum(
            low_alphanum for low_alphanum in ext_ds["low_alphanum"]
        )
        long_lines_count = sum(long_line for long_line in ext_ds["long_lines"])
        non_lexable_count = sum(not lexable for lexable in ext_ds["lexable"])

        new_dict = {
            "extension": ext,
            "language": language,
            "count": real_count,
            "low_alphanum_count": low_alphanum_count,
            "long_lines_count": long_lines_count,
            "non_lexable_count": non_lexable_count,
        }
        df = df.append(new_dict, ignore_index=True)
        print(f"New extension count: {new_dict}")

        path = f"./data/{language}/{ext}/data.json"
        ext_ds.to_json(path)
        print(f"Subset of langugae: {language}, and extension: {ext} saved")

# save the dataframe to csv
df.to_csv("./data/extension_distribution.csv")